codeforgoodconf / black_holes_frontend Goto Github PK

Keep track of the labels that a user gave and the labels that came from which person.

With this information, we can enable other features:

• treat given labels as votes, only confirming the label if there have been enough or unanimous votes.
• rate the accuracy of each user, with options to weight users' votes or reject them from participating.
• retract labels from users.
• enable training from #4 and track improvement.

TODO:

• Build a user model
• Build a vote model
• Connect spectra->vote with 1-many relationship
• Connect user->vote with 1-many relationship

Set up a way for users to log in to their account.

Add pre-commit hooks to the git repo so that when a user pushes their changes to master, it will be rejected (maybe?) if the tests do not pass.

• Run all unit tests
• Run Flake8
• Examine code coverage

Once user models are created, have the algorithm that chooses which galaxy to show a user dependent on that user.

It should

• Not show the same galaxy more than once
• Show some galaxies that we have already labeled (as a control set for the user)

Make it feel like Tinder!

• Allow users with touch screens to swipe left or right to vote yes or no.
• Test on Android and iPhones

I'm pretty sure that right now the humans input True if there IS a WR bump [meaning NOT a blackhole] and the machine marks galaxies as True if there is NO WR bump [meaning it IS a blackhole]. lets please make sure it does not do this before we use the app to train the computer!!! idk who is working on this project still but you guys can assign me if you want me to do it and ill change the html on the app to make sure it's clear and consistent.

• Also unrelated side note: I probably can't video conference with the client but you should ask him if he has a written description to put on the front page or any info for the about page so one of us can through it in there :)

Write a nice readme.

• Explain the tech stack,
• Explain the features in the website,
• Provide some explanation of the neural net,
• Provide setup instructions so new users can get involved.

We could add a "people trainer" on to the front end. The app would display a picture that has been verified as a black hole or not a black hole, and once a user guesses the answer, the app tells them whether they got it right or not.

@simian201 feel free to add more information!